Evaluation Method and Evaluation Device for Water Breakthrough Risk of Production Wells in Aquifer Drive Gas Reservoirs

ABSTRACT

The present invention provides an evaluation method and an evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers, the method comprising the following steps: building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers; acquiring weight vectors of the evaluation factors based on an analytic hierarchy process; building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors; and synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers. The present invention improves the accuracy of the evaluation result of water breakthrough risk of the gas wells, and is able to obtain an evaluation result that is more consistent with the case of actual water breakthrough of gas wells in gas reservoirs with aquifers.

This application claims the benefit of co-pending Chinese patent application 201410670301.5, filed 20 Nov. 2014, which is hereby incorporated herein as though fully set forth.

TECHNICAL FIELD

The present invention relates to the technical field of natural gas development, in particular to an evaluation method and an evaluation device for water breakthrough risk of production wells in gas reservoirs with aquifer.

DESCRIPTION OF THE RELATED ART

As for development of gas reservoirs with strong edge-bottom aquifer drive, once water breakthrough occurs in gas wells, a lot of trouble may be brought to gas field surface engineering, and meanwhile productivity of the gas wells may be decreased greatly, causing the gas wells to be shut down due to too low production rate in early period, thereby greatly influencing gas reservoirs recovery and development performance. Therefore, how to identify aquifer influx of gas wells are urgent in order to guide the well production rate optimization and improve gas field performance.

At present, main study about evaluation of water breakthrough risk of gas reservoirs focuses on prediction of water breakthrough time based on the simplified conceptual models and identification of water invasion type. Wherein, water breakthrough time prediction method adopts a water coning breakthrough time calculation formula to predict specific water breakthrough time of a gas well, but because actual gas reservoir has high heterogeneity and the production plan is continuously adjusted, use of these methods to perform evaluation of water invasion of the gas reservoirs may result in great difference with the actual situation. There are also literatures involved the material balance method and the water invasion indication curve method, but application of these methods is also limited, because these methods need too much static pressure data and good prediction can be gotten only when a certain degree of recovery is achieved, but actually, there are few testing points of static pressure and the degree of recovery of many wells when water breakthrough occurs is very low as a result, at present it is difficult to quantitatively evaluate the water breakthrough risk, which can objectively reflect the actual situation, by using existing technology.

SUMMARY OF THE INVENTION

The present invention is aimed to provide an evaluation method and an evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers, so as to make the obtained water breakthrough risk evaluation result be more consistent with the actual production characteristics.

To achieve the above purpose, on one hand, the present invention provides an evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers, comprising:

building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifer;

acquiring weight vectors of the evaluation factors based on an analytic hierarchy process;

building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifer and its evaluation factors;

synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy composite operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers.

In the evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers, according to the present invention, the evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers include the following features of gas wells in gas reservoirs with aquifer:

structure and sedimentary facies features, including structure and trap feature, fracture feature, fracture development condition, rock type and sedimentary facies;

reservoir features, including interlayer feature, reservoir type, sand body connectivity and reservoir heterogeneity;

drilling information, including drilling quality, cementing quality and distance between a perforation and the edge-bottom water;

production performance and monitoring data, including gas production profile testing result, saturation logging result and pressure transient well test evaluation result;

dynamic evaluation and prediction result, including production rate and pressure variation features, production rate transient analysis result, controllable reserves per well, water coning critical production rate and water breakthrough time prediction result.

In the evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the acquiring weight vectors of the evaluation factors based on an analytic hierarchy process specifically comprises:

performing pairwise comparison among the evaluation factors under the same hierarchy to obtain a quantized judgment matrix M;

$M = \begin{pmatrix} m_{11} & m_{12} & \ldots & m_{1\; j} \\ m_{21} & m_{22} & \ldots & m_{2\; j} \\ \vdots & \vdots & \vdots & \vdots \\ m_{i\; 1} & m_{i\; 2} & \ldots & m_{ij} \end{pmatrix}$

wherein, m_(ij) represents the weight of an evaluation factor i to an evaluation factor j;

calculating the largest eigenvalue λ_(max) of the judgment matrix M and corresponding feature vector, the feature vector being weight coefficient distribution of the evaluation factors under the same hierarchy;

performing consistency verification on the feature vector;

if the feature vector goes through the consistency verification, calculating a product Q_(i) of each row of elements of the judgment matrix M, wherein

${Q_{i} = {\prod\limits_{j = 1}^{n}\; m_{ij}}};$

calculating a n-th root

${\overset{\_}{\omega}}_{i} = \sqrt[n]{Q_{i}}$

of Q_(i) to acquire a vector ω=[ω ₁ ω ₂ . . . ω _(n)], performing a normalization process to the vector to obtain the weight vector of the evaluation factors.

In the evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers. And its evaluation factors is:

$R = {\begin{bmatrix} \left. R \middle| u_{1} \right. \\ \left. R \middle| u_{2} \right. \\ \ldots \\ \left. R \middle| u_{p} \right. \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & \ldots & r_{1\; m} \\ r_{21} & r_{22} & \ldots & r_{2\; m} \\ \vdots & \vdots & \vdots & \vdots \\ r_{p\; 1} & r_{p\; 2} & \ldots & r_{pm} \end{bmatrix}_{p,m}}$

wherein, in the fuzzy relationship matrix R, the element r_(pm) in row p column m represents a degree of membership of water breakthrough risk of gas wells in gas reservoirs with aquifers as for the feature vector from the aspect of the evaluation factor u_(p).

In the evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator specifically comprises:

according to the formula

${b_{i} = {{\sum\limits_{i = 1}^{p}\; \left( {a_{i} \cdot r_{ij}} \right)} = {\min \left( {1,{\sum\limits_{i = 1}^{p}\; {a_{i} \cdot r_{ij}}}} \right)}}},{j = 1},2,\ldots \mspace{14mu},m,$

synthesizing the fuzzy relationship matrix and the weight vectors;

wherein, b₁, a_(i), r_(ij) represent a degree of membership belonging to the jth level, a weight of the ith evaluation factor, and a degree of membership that the ith evaluation factor belongs to the jth level, respectively.

On the other hand, the present invention also provides an evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers, comprising:

an evaluation factor building module for building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers;

a weight vector acquisition module for acquiring weight vectors of the evaluation factors based on an analytic hierarchy process;

a fuzzy matrix building module for building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors;

a matrix and vector synthesizing module for synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers.

In the evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers include the following features of gas wells in gas reservoirs with aquifers:

structure and sedimentary facies features, including structure and trap feature, fracture feature, fracture development condition, rock type and sedimentary facies;

reservoir features, including interlayer feature, reservoir type, sand body connectivity and reservoir heterogeneity;

drilling information, including drilling quality, cementing quality and distance between perforations and the edge-bottom water;

production performance and monitoring data, including gas production profile testing results, saturation logging results and pressure transient well testing analysis results;

dynamic evaluation and prediction result, including production rate and pressure variation features, production rate transient analysis results, reserves controlled by a well, critical water coning production rate and the predicted water breakthrough time.

In the evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the acquiring weight vectors of the evaluation factors based on an analytic hierarchy process specifically comprises:

performing pairwise comparison among the evaluation factors under the same hierarchy to obtain a quantized judgment matrix M;

$M = \begin{pmatrix} m_{11} & m_{12} & \ldots & m_{1\; j} \\ m_{21} & m_{22} & \ldots & m_{2\; j} \\ \vdots & \vdots & \vdots & \vdots \\ m_{i\; 1} & m_{i\; 2} & \ldots & m_{ij} \end{pmatrix}$

wherein, m_(ij) represents the weight of the evaluation factor i to the evaluation factor j;

calculating the largest eigenvalue λ_(max) of the judgment matrix M and corresponding feature vector, the feature vector being weight coefficient distribution of the evaluation factors under the same hierarchy;

performing consistency verification on the feature vector;

if the eigenvector goes through the consistency verification, calculating a product Q_(i) of each row of elements of the judgment matrix M, wherein

${Q_{i} = {\prod\limits_{j = 1}^{n}\; m_{ij}}};$

calculating a n-th root

${\overset{\_}{\omega}}_{i} = \sqrt[n]{Q_{i}}$

of Q_(i) to acquire a vector ω=[ω ₁ ω ₂ . . . ω _(n)], performing a normalization process to the vector to obtain the weight vector of the evaluation factors.

In the evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors is:

$R = {\begin{bmatrix} \left. R \middle| u_{1} \right. \\ \left. R \middle| u_{2} \right. \\ \ldots \\ \left. R \middle| u_{p} \right. \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & \ldots & r_{1\; m} \\ r_{21} & r_{22} & \ldots & r_{2\; m} \\ \vdots & \vdots & \vdots & \vdots \\ r_{p\; 1} & r_{p\; 2} & \ldots & r_{pm} \end{bmatrix}_{p,m}}$

wherein, in the fuzzy relationship matrix R, the element r_(pm) in row p column m represents a degree of membership of water breakthrough risk of gas wells in gas reservoirs with aquifers as for the feature vector from the aspect of the evaluation factor u_(p).

In the evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to the present invention, the synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator specifically comprises:

according to the formula

${b_{i} = {{\sum\limits_{i = 1}^{p}\; \left( {a_{i} \cdot r_{ij}} \right)} = {\min \left( {1,{\sum\limits_{i = 1}^{p}\; {a_{i} \cdot r_{ij}}}} \right)}}},{j = 1},2,\ldots \mspace{14mu},m,$

synthesizing the fuzzy relationship matrix and the weight vectors;

wherein, b_(i), a_(i), r_(ij) represent a degree of membership belonging to the jth level, a weight of the ith evaluation factor, and a degree of membership that the ith evaluation factor belongs to the jth level, respectively.

In the present invention, a quantitative evaluation is performed on water breakthrough risk in the manner of combining an analytic hierarchy process and a fuzzy synthetic evaluation method. Because the analytic hierarchy process is a systematic research method through which evaluation and judgment are made in accordance with decomposition, comparison, judgment and comprehensive thinking method, it is more reasonable for the present invention to adopt the analytic hierarchy process to determine weight coefficients of the evaluation factors for water breakthrough risk of the gas wells, which is more adaptive to the objective fact and easy to be calculated quantitatively, thereby improving accuracy of the result of subsequent fuzzy comprehensive evaluation, and being able to obtain an evaluation result that is more consistent with the case of actual water breakthrough situation of gas wells in gas reservoirs with aquifers.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanied drawings described herein are used for providing further understanding to the present invention, which constitute a part of the present application but do not constitute any definition to the present invention. In the drawings:

FIG. 1 is a flowchart of an evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with an embodiment of the present invention;

FIG. 2a is a schematic of a curve of contrast relationship between actual production rate of a well A in the evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with the embodiment of the present invention and a water coning critical production rate calculated based on Chaperon method;

FIG. 2b is a schematic of a curve of contrast relationship between actual production rate of a well B in the evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with the embodiment of the present invention and a water coning critical production rate calculated based on Chaperon method;

FIG. 2c is a schematic of a curve of contrast relationship between actual production rate of a well C in the evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with the embodiment of the present invention and a water coning critical production rate calculated based on Chaperon method;

FIG. 2d is a schematic of a curve of contrast relationship between actual production rate of a well D in the evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with the embodiment of the present invention and a water coning critical production rate calculated based on Chaperon method.

DETAILED DESCRIPTION

Hereinafter, the present invention is further described in details in combination with the embodiment and the accompanied drawings, in order to describe purpose, technical solution and advantages of the present invention is clearer. Here the schematic embodiment of the present invention and its description are to explain the present invention, but are not to define the present invention.

Hereinafter the specific embodiment of the present invention is further described in detail in combination with the accompanied drawings.

As shown in FIG. 1, an evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with an embodiment of the present invention comprises the following steps:

step S101: building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers. In the embodiment of the present invention, the evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers are as shown in Table 1:

TABLE 1 Two-hierarchy evaluation factors of water breakthrough risk 1st-hierarchy evaluation factors 2nd-hierarchy evaluation factors A Structure and sedimentary a1 structure and trap feature facies features a2 fracture feature a3 fracture development condition a4 rock type and sedimentary facies B Reservoir feature b1 interlayer feature b2 reservoir type b3 sand body connectivity b4 reservoir heterogeneity C Drilling and completion c1 drilling quality information c2 cementing quality c3 distance between a perforation and the edge-bottom water D production performance and d1 gas production profile testing result monitoring data d2 saturation logging result d3 transient well testing analysis result E Dynamic evaluation and e1 production rate and pressure variation features prediction result e2 production rate transient analysis e3 reserve controlled by single well e4 water coning critical production rate e5 water breakthrough time prediction result

wherein, water invasion actually has great influence on production performance of the gas well, the gas well is not subjected to any pressure support when no water invasion occurs, the production performance of the gas well is shown as features of closed gas reservoir. At the initial stage of water invasion, the gas well is supported with water body energy, pressure may decline more slowly in the case of stable production rate than the case that no water invasion occurs. In the middle and later periods of water invasion, especially when edge-bottom water rushes into around the gas wells, gas flow is obviously resisted, gas production pressure difference in the case of same production rate increase obviously. Production characteristics of water invasion at different stages are summarized as shown in Table 2. Table 2 only exemplarily describes recognition of water invasion by production performance changes in the case of consistent production rate or stable flowing pressure, and similar analysis can be performed in the mode on the actual production data of the gas well, and pressure complex production performance changes.

TABLE 2 Diagnose of water invasion by production performance data of the gas well serial number type production performance features 1 Non-water consistent production rate, pressure declining tendency is in invasion type consistency stable pressure, production rate declining tendency is in consistency 2 Initial stage of consistent production rate, pressure declines more slowly water invasion type than the previous stage stable pressure, production rate declines more slowly than the previous stage 3 Middle and consistent production rate, pressure declines faster than the later periods of previous stage water invasion type stable pressure, production rate declines faster than the previous stage

Furthermore, in the embodiment of the present invention, evaluation can be performed on water coning critical production rate per well by selecting an evaluation method suitable for evaluating water coning critical production rate of gas reservoir, and a contrast curve of actual daily gas production rate per well and water coning critical production rate is drawn, so that water invasion or water breakthrough of the gas well can be recognized according to relationship between gas well production rate and water coning critical production rate. FIGS. 2a to 2d provide four curves of contrast relationships between four possible actual production rate of the gas well A, B, C, D and water coning critical production rate calculated based on Chaperon method. From the aspect of relationship between gas well production rate and water coning critical production rate, four wells in FIGS. 2a to 2d are sequenced as D, C, B, A in accordance with possible water breakthrough levels, that is, water breakthrough occurs most easily in the well D, because the production rate of D is higher than the water coning critical production rate of D all the time, water cone is very easily formed or it causes edge water tongue advance. Water breakthrough occurs latest in the well A, because production rate of A is lower than the water coning critical production rate of A all the time, and water cone is not formed. Certainly, it is also possible to judge water invasion stage of gas wells by using a Blasingame production rate transient analysis type curve, a flowing material balance curve and the like, and the wells are categorized, and water breakthrough time of the gas wells can be preliminarily evaluated.

Step S102: acquiring weight vectors of the evaluation factors based on an analytic hierarchy process. Specifically:

firstly, performing pairwise comparison among the evaluation factors under the same hierarchy to obtain a quantized judgment matrix M;

$M = \begin{pmatrix} m_{11} & m_{12} & \ldots & m_{1\; j} \\ m_{21} & m_{22} & \ldots & m_{2\; j} \\ \vdots & \vdots & \vdots & \vdots \\ m_{i\; 1} & m_{i\; 2} & \ldots & m_{ij} \end{pmatrix}$

wherein, m_(ij) represents the weight of an evaluation factor i to an evaluation factor j;

then, calculating the largest eigenvalue λ_(max) of the judgment matrix M and corresponding feature vector, the feature vector being weight coefficient distribution of the evaluation factors under the same hierarchy, which represents importance ranking of the evaluation factors under the same hierarchy;

secondly, performing consistency verification on the feature vector; in order to judge whether the feature factor is valid, it is necessary to perform consistency verification on the judgment matrix M and to calculate a consistency index

${CI} = \frac{\lambda_{\max} - n}{n - 1}$

of the deviation, in which 500 sample matrices are constructed by a random method, the construction method is to fill upper triangular items of the sample matrices randomly with scales and their reciprocals, numerical values of the principal diagonal are always 1, and corresponding transposition position items are reciprocals of the above corresponding position random numbers. Then consistency index values of the random sample matrices are calculated, the CI values are averaged to obtain an average random consistency index RI value. When random consistency ratio is

${{CR} = {\frac{CI}{RI} < 0.10}},$

it is regarded that a result of the analytic hierarchy process has a satisfactory consistency, that is, distribution of weight coefficients is reasonable; otherwise, element values of the judgment matrix M are reset, and values of the weight coefficients are re-distributed.

Thirdly, if the feature vector goes through the consistency verification, calculating a product Q_(i) of each row of elements of the judgment matrix M, wherein

${Q_{i} = {\prod\limits_{j = 1}^{n}\; m_{ij}}};$

finally, calculating a n-th root

${\overset{\_}{\omega}}_{i} = \sqrt[n]{Q_{i}}$

of Q_(i) to acquire a vector ω=[ω ₁,ω ₂ . . . ω _(n)], performing a normalization process to the vector to obtain the weight vector of the evaluation factors.

Step S03: building a fuzzy relationship matrix between the water breakthrough risks of gas wells in gas reservoirs with aquifers and its evaluation factors.

$R = {\begin{bmatrix} \left. R \middle| u_{1} \right. \\ \left. R \middle| u_{2} \right. \\ \ldots \\ \left. R \middle| u_{p} \right. \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & \ldots & r_{1\; m} \\ r_{21} & r_{22} & \ldots & r_{2\; m} \\ \vdots & \vdots & \vdots & \vdots \\ r_{p\; 1} & r_{p\; 2} & \ldots & r_{pm} \end{bmatrix}_{p,m}}$

wherein, in the fuzzy relationship matrix R, the element r_(pm) in row p column m represents a degree of membership of water breakthrough risk of gas wells in gas reservoirs with aquifers as for the feature vector from the aspect of the evaluation factor u_(p).

Step S104: combining the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation results of water breakthrough risk of gas wells in gas reservoirs with aquifers. Specifically:

For a maxi-min algorithm commonly used in fuzzy comprehensive evaluation, when there are many evaluation factors, weights distributed to each evaluation factor are often very small, such that in fuzzy composite operation, much information is lost, which often causes the results to be difficult to distinguish and results in unreasonable (that is, model inefficiency) case. Therefore, focusing on the above problem, here weighted average type of fuzzy composite operators are adopted to reduce information loss, the calculation formula is:

${b_{i} = {{\sum\limits_{i = 1}^{p}\; \left( {a_{i} \cdot r_{ij}} \right)} = {\min \left( {1,{\sum\limits_{i = 1}^{p}\; {a_{i} \cdot r_{ij}}}} \right)}}},{j = 1},2,\ldots \mspace{14mu},m$

wherein, b_(i), a_(i), r_(ij) represent a degree of membership belonging to the jth level, a weight of the ith evaluation factor, and a degree of membership that the ith evaluation factor belongs to the jth level, respectively.

When there are multiple individual wells, a comprehensive evaluation result of water breakthrough risk of each individual well can be obtained in accordance with the above evaluation method. Furthermore, if there is a need, the obtained comprehensive evaluation results of water breakthrough risk of the multiple individual wells can be graded and ranked, for example, the multiple individual wells are ranked in terms of level and position according to the comprehensive evaluation results of water breakthrough risk by using a method of seeking a grade of membership by the weighted average.

Furthermore, the evaluation method in accordance with the embodiment of the present invention has been applied successfully in gas fields of Tarim oilfield, PetroChina, which has a high prediction accuracy. Through comprehensive evaluation of water breakthrough risk of the gas fields, production rate of the gas wells are optimized and adjusted, on the basis of stable overall production rate of the gas fields, early water breakthrough in gas wells can be avoided to prevent the production rate decrease severely and improve the gas recovery, thereby greatly improving development effect of the gas fields. In addition, the practical application result shows that, the evaluation method in accordance with the embodiment of the present invention is at least applicable to evaluation of water breakthrough risk of domestic large-scale gas reservoirs with edge-bottom aquifer.

In the embodiment of the present invention, a quantitative evaluation is performed on water breakthrough risk in the manner of combining an analytic hierarchy process and a fuzzy synthetic evaluation method. Because the analytic hierarchy process is a systematic research method through which evaluation and judgment are made in accordance with decomposition, comparison and judgment and comprehensive thinking method, it is more reasonable for the embodiment of the present invention to adopt the analytic hierarchy process to determine weight coefficients of the evaluation factors for water breakthrough risk of the gas well, which is more adaptive to the objective fact and is easy to express quantitatively, thereby improving accuracy of the result of subsequent fuzzy comprehensive evaluation.

Correspondingly to the above evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers, the evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers in accordance with the embodiment of the present invention comprises an evaluation factor building module, a weight vector acquisition module, a fussy matrix building module and a matrix and vector synthesizing module, wherein:

the evaluation factor building module is used for building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers. Specific evaluation factors can be seen in the step S101 of the above method of the embodiment.

The weight vector acquisition module is used for acquiring weight vectors of the evaluation factors based on an analytic hierarchy process. Specific content can be seen in the step S102 of the above method of the embodiment.

The fuzzy matrix building module is used for building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors. Specific content can be seen in the step S103 of the above method of the embodiment.

The matrix and vector synthesizing module is used for synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers. Specific content can be seen in the step S104 of the above method of the embodiment.

In the embodiment of the present invention, a quantitative evaluation is performed on water breakthrough risk in the manner of combining an analytic hierarchy process and a fuzzy synthetic evaluation method. Because the analytic hierarchy process is a systematic research method through which evaluation and judgment are made in accordance with decomposition, comparison and judgment and comprehensive thinking method, it is more reasonable for the embodiment of the present invention to adopt the analytic hierarchy process to determine weight coefficients of the evaluation factors for water breakthrough risk of the gas well, which is more adaptive to the objective fact and is easy to express quantitatively, thereby improving accuracy of the result of subsequent fuzzy comprehensive evaluation.

Persons skilled in the art also know that, the illustrative logic modules, units and steps listed in the embodiment of the present invention can be implemented by hardware, software or combination of the two. Implementation by hardware or software depends on specific application and design requirement of the whole system. For each specific application, persons skilled in the art can use various methods to realize the functions, but such implementation shall not be understood as going beyond the scope claimed by the embodiment of the present invention.

Various illustrative logic modules or units described in the embodiment of the present invention can achieve or operate the described functions through the design of a universal processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array or other programmable logic devices, a discrete gate or transistor logic, a discrete hardware component or any combination of the above. The universal processor may be a microprocessor, and alternatively, the universal processor may be any traditional processor, controller, microcontroller or state machine. The processor also can be implemented by combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors united with one digital signal processor core, or any other similar configuration.

The steps of the method or algorithm described in the embodiment of the present invention can be directly inserted into hardware, a software module executed by the processor, or combination thereof The software module can be stored in an RAM memory, a flash memory, an ROM memory, an EPROM memory, an EEPROM memory, a resistor, a disk, a removable disk, CD-ROM or storage medium in any other forms in the technical field. Illustratively, a storage medium can be connected to the processor, so that the processor can read information from the storage medium and can write and store information into the storage medium. Alternatively, the storage medium also can be integrated into the processor. The processor and the storage medium can be disposed in the ASIC, and the ASIC can be disposed in a user terminal. Alternatively, the processor and the storage medium can be disposed in different components in the user terminal.

In one or more illustrative designs, the above functions described in the embodiment of the present invention can be realized by hardware, software, firmware or any combination thereof If the functions are realized in software, then the functions can be stored in a computer-readable medium, or can be transmitted to the computer-readable medium in the form of one or more instructs or codes. The computer-readable medium includes a computer storage medium and a communication medium that facilitates transfer of computer program from one place to other places. The storage medium can be any available media that can be accessed by a general or special computer. For example, such computer-readable media can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disc storage or other magnetic storage device, or any other medium that can be used for carrying or storing program codes in the form of instructs or data structure and other forms that can be read by a general or special computer or a general or special processor. Furthermore, any connection can be suitably defined as computer-readable medium, for example, if software is transferred from a website, a server or other remote resources by a coaxial cable, an optical fiber cable, a twisted pair, a digital user line (DSL) or in a wireless manner such as infrared, wireless or microwave or the like, and can also be included in the defined computer-readable medium. The described disk and disc include a compressed disc, a laser disc, an optical disc, a DVD, a floppy disc and a blue-ray disc, wherein the disc generally copies data with magnetism, while disk generally copies data optically with laser. The above-described combination also can be included in the computer-readable medium.

The above specific embodiment further describes in detail the object, the technical solution and beneficial effect of the present invention. It shall be understood that, the above description is merely a specific embodiment of the present invention, but not for limiting the protection scope of the invention. Any modification, equivalent replacement, improvement and the like within the spirit and principle of the present invention shall be included within the protection scope of the present invention. 

1. An evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers, comprising: building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers; acquiring weight vectors of the evaluation factors based on an analytic hierarchy process; building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors; and synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers.
 2. The evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 1, characterized in that, the evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers include the following features of gas wells in gas reservoirs with aquifers: structure and sedimentary facies features, including structure and trap feature, fracture feature, fracture development condition and rock type and sedimentary facies; reservoir features, including interlayer feature, reservoir type, sand body connectivity and reservoir heterogeneity; drilling and completion information, including drilling quality, cementing quality and distance between a perforation and the edge-bottom water; production performance and monitoring data, including gas production profile testing result, saturation logging result and transient well testing analysis result; and dynamic evaluation and prediction result, including production rate and pressure variation characteristics, production rate transient analysis result, reserve controlled by a single well, water coning critical production rate and water breakthrough time prediction result.
 3. The evaluation method for water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 1, characterized in that, the acquiring weight vectors of the evaluation factors based on an analytic hierarchy process specifically comprises: performing pairwise comparison among the evaluation factors under the same hierarchy to obtain a quantized judgment matrix M; $M = \begin{pmatrix} m_{11} & m_{12} & \ldots & m_{1\; j} \\ m_{21} & m_{22} & \ldots & m_{2\; j} \\ \vdots & \vdots & \vdots & \vdots \\ m_{i\; 1} & m_{i\; 2} & \ldots & m_{ij} \end{pmatrix}$ wherein, m_(ij) represents the weight of an evaluation factor i to an evaluation factor j; calculating the largest eigenvalue λ_(max) of the judgment matrix M and corresponding feature vector, the feature vector being weight coefficient distribution of the evaluation factors under the same hierarchy; performing consistency verification on the feature vector; if the feature vector goes through the consistency verification, calculating a product Q_(i) of each row of elements of the judgment matrix M, wherein ${Q_{i} = {\prod\limits_{j = 1}^{n}\; m_{ij}}};$ calculating a n-th root ${\overset{\_}{\omega}}_{i} = \sqrt[n]{Q_{i}}$ of Q_(i) to acquire a vector ω=[ω ₁ ω ₂ . . . ω _(n)], performing a normalization process to the vector to obtain the weight vector of the evaluation factors.
 4. The evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 1, characterized in that, the fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors is: $R = {\begin{bmatrix} \left. R \middle| u_{1} \right. \\ \left. R \middle| u_{1} \right. \\ \ldots \\ \left. R \middle| u_{p} \right. \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & \ldots & r_{1\; m} \\ r_{21} & r_{22} & \ldots & r_{2\; m} \\ \vdots & \vdots & \vdots & \vdots \\ r_{p\; 1} & r_{p\; 2} & \ldots & r_{pm} \end{bmatrix}_{p,m}}$ wherein, in the fuzzy relationship matrix, the element r_(pm) in row p column m represents a degree of membership of water breakthrough risk of gas wells in gas reservoirs with aquifers as for the feature vector from the aspect of the evaluation factor u_(p).
 5. The evaluation method for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers. according to claim 1, characterized in that, the synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator specifically comprises: according to the formula ${b_{i} = {{\sum\limits_{i = 1}^{p}\; \left( {a_{i} \cdot r_{ij}} \right)} = {\min \left( {1,{\sum\limits_{i = 1}^{p}\; {a_{i} \cdot r_{ij}}}} \right)}}},{j = 1},2,\ldots \mspace{14mu},m,$ synthesizing the fuzzy relationship matrix and the weight vectors; wherein, b_(i), a_(i), r_(ij) represent a degree of membership belonging to the jth level, a weight of the ith evaluation factor, and a degree of membership that the ith evaluation factor belongs to the jth level, respectively.
 6. An evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers, comprising: an evaluation factor building module for building evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers; a weight vector acquisition module for acquiring weight vectors of the evaluation factors based on an analytic hierarchy process; a fuzzy matrix building module for building a fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors; a matrix and vector synthesizing module for synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator, to acquire a comprehensive evaluation result of water breakthrough risk of gas wells in gas reservoirs with aquifers.
 7. The evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 6, characterized in that, the evaluation factors that influence water breakthrough risk of gas wells in gas reservoirs with aquifers include the following features of gas wells in gas reservoirs with aquifers: structure and sedimentary facies features, including structure and trap feature, fracture feature, fracture development condition and rock type and sedimentary facies; reservoir features, including interlayer feature, reservoir type, sand body connectivity and reservoir heterogeneity; drilling and completion information, including drilling quality, cementing quality and distance between a perforation and the edge-bottom water; production performance and monitoring data, including gas production profile testing result, saturation logging result and transient well testing analysis result; dynamic evaluation and prediction result, including production rate and pressure variation characteristics, production rate transient analysis result, reserve controlled by a single well, water coning critical production rate and water breakthrough time prediction result.
 8. The evaluation device for water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 6, characterized in that, the acquiring weight vectors of the evaluation factors based on an analytic hierarchy process specifically comprises: performing pairwise comparison among the evaluation factors under the same hierarchy to obtain a quantized judgment matrix M; $M = \begin{pmatrix} m_{11} & m_{12} & \ldots & m_{1\; j} \\ m_{21} & m_{22} & \ldots & m_{2\; j} \\ \vdots & \vdots & \vdots & \vdots \\ m_{i\; 1} & m_{i\; 2} & \ldots & m_{ij} \end{pmatrix}$ wherein, m_(ij) represents the weight of an evaluation factor i to an evaluation factor j; calculating the largest eigenvalue λ_(max) of the judgment matrix M and corresponding feature vector, the feature vector being weight coefficient distribution of the evaluation factors under the same hierarchy; performing consistency verification on the feature vector; if the feature vector goes through the consistency verification, calculating a product Q_(i) of each row of elements of the judgment matrix M, wherein ${Q_{i} = {\prod\limits_{j = 1}^{n}\; m_{ij}}};$ calculating a n-th root ${\overset{\_}{\omega}}_{i} = \sqrt[n]{Q_{i}}$ of Q_(i) to acquire a vector ω=[ω ₁ ω ₂ . . . ω _(n)], performing a normalization process to the vector to obtain the weight vector of the evaluation factors.
 9. The evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 6, characterized in that, the fuzzy relationship matrix between the water breakthrough risk of gas wells in gas reservoirs with aquifers and its evaluation factors is: $R = {\begin{bmatrix} \left. R \middle| u_{1} \right. \\ \left. R \middle| u_{1} \right. \\ \ldots \\ \left. R \middle| u_{p} \right. \end{bmatrix} = \begin{bmatrix} r_{11} & r_{12} & \ldots & r_{1\; m} \\ r_{21} & r_{22} & \ldots & r_{2\; m} \\ \vdots & \vdots & \vdots & \vdots \\ r_{p\; 1} & r_{p\; 2} & \ldots & r_{pm} \end{bmatrix}_{p,m}}$ wherein, in the fuzzy relationship matrix R, the element r_(pm) in row p column m represents a degree of membership of water breakthrough risk of gas wells in gas reservoirs with aquifers as for the feature vector from the aspect of the evaluation factor u_(p).
 10. The evaluation device for evaluating water breakthrough risk of gas wells in gas reservoirs with aquifers according to claim 6, characterized in that, the synthesizing the fuzzy relationship matrix and the weight vectors according to a weighted average fuzzy synthesis operator specifically comprises: according to the formula ${b_{i} = {{\sum\limits_{i = 1}^{p}\; \left( {a_{i} \cdot r_{ij}} \right)} = {\min \left( {1,{\sum\limits_{i = 1}^{p}\; {a_{i} \cdot r_{ij}}}} \right)}}},{j = 1},2,\ldots \mspace{14mu},m,$ synthesizing the fuzzy relationship matrix and the weight vectors; wherein, b_(i), a_(i), r_(ij) represent a degree of membership belonging to the jth level, a weight of the ith evaluation factor, and a degree of membership that the ith evaluation factor belongs to the jth level, respectively. 